Internet search engines are designed to locate desired information from amongst the vast amount of information contained across the Internet. Users describe the information they are looking for by entering queries containing search terms. The search engine matches the search terms against an index of Web pages using a variety of relevance calculations with the objective of identifying those Web pages that are most likely related to the information sought by the users. The search engine then returns a ranked list of hyperlinks to these Web pages, with links to those pages thought to be most relevant nearer the top of the list.
The objective of search engines is to deliver the most relevant Web pages for a given query. Early search engines determined the relevance of Web pages using information contained within each page, such as the presence, density, and proximity of the search terms within the document. More advanced search engines consider information relating to links between the Web pages when determining relevance.
The process of determining which Web pages are most relevant is very difficult because the number of Web pages on the Internet is very large and growing, and there are often a large number of Web pages that nominally satisfy the users' queries. Many different concepts can be referred to with the same or similar terms. Most users are not sophisticated in the process of creating and entering well-formed queries, so there is ambiguity in what type of information they are seeking.
The results returned by search engines are susceptible to manipulation. Web site operators can add content or meta data or hyperlinks from other Web sites to their Web pages, with the objective of having search engines return links to their Web pages high in the ranked list of results. This has the result that some Web pages which do not contain the information users are seeking are ranked high in the results list, thus reducing the overall perceived accuracy of the search result. This practice is often referred to as Search Engine Optimization (or “SEO”). Search engines must expend an ever-increasing effort just to maintain a consistent level of relevance in the face of SEO. It would be desirable for a search engine to be able to gather feedback directly from users to determine which Web pages satisfied or did not satisfy their query, in order to deliver more relevant results for subsequent queries by the same or different users.
Over time, the operators of search engines make adjustments to the methods used to determine relevance, and to the weights applied to each of these methods in order to maintain or improve the accuracy of their search engine. This process usually involves experimentation, such as having test users score the results delivered by the search engine for different queries. The scores can be compiled and analyzed in order to decide what methods to use or what weights to apply. This process is time consuming, imprecise, unrepresentative and inflexible. It would be desirable to use a feedback mechanism that took input directly from real users, and adjusted the search relevance methods and weights to increase the accuracy of the search engine.
Additionally, users have different intentions when they enter a query because they may have a different understanding of the search terms, have different tastes and interests, or are in a different “mode” of search. For example, if three different users enter the query “iPod”, the first user may be shopping for an iPod, the second user may be searching for news about iPod, and the third user may be searching for information or reviews about iPod. Users could give some kind of indication about their interests and objectives when conducting a search. Therefore it would be desirable to develop a search engine that was able to consider the different modes of searching and the differences between the interests of users when determining which results are relevant to the users queries.